Evaluation of inter-patient variability in the pharmacodynamic indices of warfarin

Evaluation of supervised machine learning algorithms in predicting the poor anticoagulation control and stable weekly doses of warfarin

BACKGROUND

Machine learning algorithms (MLAs) carry a huge potential in identifying predicting factors and are being explored for their utility in the field of personalized medicine.

AIM

We aimed to investigate MLAs for identifying predictors (clinical and genetic) of poor anticoagulation status (ACS) and stable weekly warfarin dose (SWWD).

METHOD

Clinical factors, in addition to the CYP2C9, VKORC1, and CYP4F2 genotypes, were obtained for patients receiving warfarin for at least the previous six months. The C5.0 decision tree classification algorithm was used to predict poor ACS while classification and regression tree analysis (CART), in addition to the Chi-square automatic interaction detector (CHAID), was used to predict SWWD. The percentage of patients within 20% of the actual dose, root mean squared error (RMSE), and area under the receiver-operating characteristics curve (AUROC) were identified as performance indicators of the models.

RESULTS

In the C5.0 classification decision tree, the CYP4F2 genotype was the strongest predictor of ACS (AUROC = 0.53). In the CART analysis of SWWD, VKORC1 polymorphisms were the most significant predictor, followed by the CYP2C9 genotype (percentage of patients within 20% of the actual dose = 38.2%, RMSE = 13.6). For the CHAID algorithm, the percentage of patients within 20% of the actual dose was 49%, while the RMSE was found to be 13.4.

CONCLUSION

Genetic and non-genetic predictive factors were identified by the MLAs for ACS and SWWD. Further, the need to externally validate the MLAs in a prospective study was highlighted.

Is there a circannual variation in the anticoagulation control of warfarin?

BACKGROUND

The literature regarding the seasonal variation in the therapeutic response to warfarin is somewhat contradictory, with several discrepancies. We assessed the influence of seasons on various pharmacodynamic indices of warfarin.

METHODS

A retrospective study was carried out in adults receiving warfarin for at least 6 months. Details of their demographic characteristics, duration and dose of warfarin therapy and values of prothrombin time international normalised ratio (PT-INR) were retrieved. Standard definitions were followed for defining various seasons, time in therapeutic range (TTR), log-INR variability and warfarin sensitivity index (WSI). National Institute for Health and Care Excellence (NICE) criteria were used for defining TTR into good (≥65%) and poor (<65%) anticoagulation control.

RESULTS

Two hundred and four patients were recruited. Only a subtle statistically significant difference was observed between the numbers of patients in the various PT-INR categories. However, no significant intra-individual differences were observed in mean TTR. Similarly, the proportion of patients with poor anticoagulation control, high INR variability and high WSI was not significantly different between summer, transition period 1, winter and transition period 2.

CONCLUSION

No clinically significant seasonal variations were observed in the therapeutic response to warfarin.

Influence of CYP2C9, VKORC1, and CYP4F2 polymorphisms on the pharmacodynamic parameters of warfarin: a cross-sectional study

BACKGROUND

Warfarin is the most commonly evaluated drug in pharmacogenetic-guided dosing studies. However, gaps remain regarding the influence of the genetic polymorphisms of CYP2C9, VKORC1, and CYP4F2 on specific pharmacodynamic parameters like the warfarin sensitivity index (WSI), prothrombin time international normalized ratio (PT-INR), and log-INR variability.

METHODS

A cross-sectional study was conducted in non-smoking adults receiving warfarin for at least 6 months. Their demographics, diagnoses, warfarin dosing regimen, concomitant drugs, PT-INR, and bleeding episodes were obtained. CYP2C9 (rs1057910-*3 and rs1799853-*2 alleles), CYP4F2 (rs2108622), and VKORC1 (rs9923231) polymorphisms were assessed using real-time polymerase chain reaction. Three genotype groups (I-III) were defined based on the combined genetic polymorphisms of CYP2C9 and VKORC1 from the FDA's recommendations. Key outcome measures included anticoagulation control, time spent in therapeutic range, stable warfarin dose, WSI, log-INR variability, and Warfarin Composite Measure (WCM).

RESULTS

The study recruited 236 patients; 75 (31.8%) carried a functional CYP2C9 variant allele, and, 143 (60.6%) had at least one T allele in CYP4F2 and 133 (56.4%) had at least one T allele in VKORC1. Groups' II and III CYP2C9 and VKORC1 genotypes were observed with reduced stable warfarin dose, increased WSI, higher log-INR variability, and increased bleeding risk. The presence of *2 or *3 allele in CYP2C9 was observed with reduced stable warfarin doses akin to the presence of T alleles in VKORC1; however, the doses increased with T alleles in CYP4F2.

CONCLUSION

The evaluated genetic polymorphisms significantly influenced all the pharmacodynamic parameters of warfarin. Evaluating CYP2C9, VKORC1, and CYP4F2 genetic polymorphisms prior to warfarin initiation is likely to optimize therapeutic response.

Evaluation of pharmacokinetics of warfarin from validated pharmacokinetic-pharmacodynamic model

Background

Pharmacokinetics of warfarin has not been described in our population. We derived the pharmacokinetic parameters from a validated pharmacokinetic-pharmacodynamic model.

Methods

Patients receiving warfarin for at least 6 months were recruited and their demographic characteristics, prothrombin time international normalized ratio (PT-INR), warfarin doses and concomitant drugs were collected. Using a validated pharmacokinetic-pharmacodynamic model, we predicted maximum plasma concentration (C_max), total clearance (C_L), volume of distribution (V_d) and elimination rate (k). Warfarin sensitive index (WSI) and warfarin composite measures (WCM) were estimated from the dose and INR values. Liver weight was predicted using validated formula.

Results

Two-hundred and twenty patients were recruited. The following were the predicted pharmacokinetic parameters: C_max (mg/L) was 5.8 (0.4); k (L/day) was 1 (0.1); CL (L/day) was 2.1 (0.2); and V_d (L) was 7.6 (0.2). Patients with C_max and elimination rate outside the mean+1.96 SD had significantly lower WSI and higher WCM. Significant correlations were observed between C_max with CL, V_d, and k of warfarin. Significant correlations were also observed between CL and V_d of warfarin with liver weight of the study participants.

Conclusion

We predicted pharmacokinetic parameters of warfarin from the validated pharmacokinetic-pharmacodynamic model in our population. More studies are needed exploring the relationship between various pharmacodynamic indices of warfarin and pharmacokinetic parameters of warfarin.

The Comparison of Therapeutic Efficacy Between Dabigatran Versus Warfarin in Patients With Nonvalvular Atrial Fibrillation

BACKGROUND

Novel oral anticoagulants and warfarin are widely used for stroke prevention in patients with atrial fibrillation. The anticoagulation status of patients receiving warfarin or rivaroxaban has been studied. In this study, we aimed to evaluate the effect of dabigatran and warfarin on preventing thrombin generation (TG).

METHODS

This retrospective study enrolled 237 nonvalvular atrial fibrillation (NVAF) subjects treated with 110 mg dabigatran etexilate twice daily and 224 NVAF patients received adjusted-dose warfarin (international normalized ratio [INR] of 2 to 3)). Coagulation assays, prothrombin fragment 1  +  2 (F1+2), calibrated automated thrombogram, and thrombin-antithrombin complex (TAT) were detected at the steady state.

RESULTS

Activated partial thromboplastin time (APTT), antithrombin III activity, fibrinogen, and lag time showed no difference between the two groups. Compared to the dabigatran group, prothrombin time and INR values were higher in the warfarin group (all P < .001). Thrombin time, endogenous thrombin potential, peak TG (Cmax), F1+2, and TAT were lower in the warfarin group. The inhibition of TG was still stronger in the warfarin group when the patients were divided into subgroups.

CONCLUSION

Conventional coagulation assays are suboptimal for assessing the coagulation status of dabigatran. TG could be used as supplementary assays to evaluate the anticoagulation effect of oral anticoagulants. Our results suggest that warfarin may inhibit TG more aggressively than dabigatran in patients regardless of age and kidney function.